Physics-of-failure-based prognostics for electronic products

Abstract

This paper presents a physics-of-failure (PoF)-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life cycle loading and failure mechanisms to perform reliability design and assessment. PoF-based prognostics permit the assessment of product reliability under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition (ie, the product's 'health') and the prediction of the future state of reliability. A formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life cycle loads, damage accumulation, and assessment of uncertainty, is presented. Then, applications of PoF-based prognostics are discussed.

Lifetime prediction of vehicle components using online monitoring data

“…SOE data is dynamic covariates, and can predict lifetime characteristics in real time based on time-series analysis. By contrast, Lee, et al (2014) and Pecht and Gu (2009) described the study of PHM (Prognostics and Health Management). PHM is the methods that can be diagnose the sign of failure, and evaluate the health state from covariates directory related to the failure mechanism.…”

A Prognostics and Health Management for Information and Electronics-Rich Systems

“…9) To implement this approach, the potential failure modes, mechanisms, and sites of the system based on the lifecycle loading conditions must be identified. The stress at each failure site is obtained as a function of loading conditions, geometry and material properties of the system.…”

Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm

“…[3][4][5][6] There are several RUL prognostic approaches in the existing literature that can be grouped into two categories: data driven and physics of failure (PoF). 7,8 PoF methods are based on the physical mode, which make use of the knowledge of fault mechanism and life cycle load of electronics equipment to design and evaluate its reliability. Data-driven methods analyze the data that can be easily obtained by sensors and are relatively easy to implement due to ignoring the physical principles and factors, such as temperature in the model.…”